Embedded Computing Design

The rapid growth of artificial intelligence and machine learning has led to the development of numerous deep learning frameworks. Each framework has its strengths and weaknesses, making it challenging to deploy models across different platforms. However, the Open Neural Network Exchange (ONNX) framework has emerged as a powerful solution to this problem. This article introduces the ONNX framework, explains its basics, and highlights the benefits of using it.

In contrast to traditional mass media, such as printed material or audio recordings, which feature little to no interaction between users, a multimedia is a form of communication that uses a combination of different content forms such as audio, text, animations, images, or video into a single interactive presentation. This definition now seems outdated because coming to 2022, multimedia has just exploded with more complex forms of interactions.

Deep learning is growing at a tremendous pace in terms of models and their datasets. In terms of applications, the deep learning market is dominated by image recognition followed by optical character recognition, and facial and object recognition. According to Allied market research, the global deep learning market was valued at $6.85 billion in 2020, and it is predicted to reach $179.96 billion by 2030, with a CAGR of 39.2% percent from 2021 to 2030.

AI has become ubiquitous today from personal devices to enterprise applications, you see them everywhere. The advent of IoT clubbed with rising demand for data privacy, low power, low latency, and bandwidth constraints has increasingly pushed for AI models to be running at the edge instead of the cloud.